Databricks Migration Workshop: From Stored Procedures to Lakehouse (5-Day Intensive) Training Course

Introduction, Objectives, and Migration Strategy

• Course aims, alignment with participant profiles, and success metrics
• Overview of high-level migration strategies and risk assessments
• Configuration of workspaces, repositories, and lab datasets

Day 1 — Migration Fundamentals and Architecture

• Lakehouse concepts, Delta Lake introduction, and Databricks architecture
• Differences between SMP and MPP and their impact on migration
• Medallion (Bronze→Silver→Gold) design principles and Unity Catalog overview

Day 1 Lab — Translating a Stored Procedure

• Practical migration of a sample stored procedure to a notebook
• Converting temporary tables and cursors into DataFrame transformations
• Verification and comparison against the original output

Day 2 — Advanced Delta Lake & Incremental Loading

• ACID transactions, commit logs, versioning, and time travel features
• Auto Loader, MERGE INTO patterns, upserts, and schema evolution
• OPTIMIZE, VACUUM, Z-ORDER, partitioning, and storage optimisation

Day 2 Lab — Incremental Ingestion & Optimization

• Implementing Auto Loader ingestion and MERGE workflows
• Applying OPTIMIZE, Z-ORDER, and VACUUM; validating outcomes
• Evaluating read/write performance enhancements

Day 3 — SQL in Databricks, Performance & Debugging

• Analytical SQL capabilities: window functions, higher-order functions, JSON/array processing
• Interpreting the Spark UI: DAGs, shuffles, stages, tasks, and bottleneck analysis
• Query optimisation techniques: broadcast joins, hints, caching, and spill mitigation

Day 3 Lab — SQL Refactoring & Performance Tuning

• Refactoring a resource-intensive SQL process into optimised Spark SQL
• Utilising Spark UI traces to identify and resolve skew and shuffle issues
• Benchmarking performance before and after tuning; documenting the process

Day 4 — Tactical PySpark: Replacing Procedural Logic

• Spark execution model: driver, executors, lazy evaluation, and partitioning strategies
• Converting loops and cursors into vectorised DataFrame operations
• Modularisation, UDFs/pandas UDFs, widgets, and reusable libraries

Day 4 Lab — Refactoring Procedural Scripts

• Refactoring a procedural ETL script into modular PySpark notebooks
• Incorporating parametrisation, unit-style tests, and reusable functions
• Code review and application of best-practice checklists

Day 5 — Orchestration, End-to-End Pipeline & Best Practices

• Databricks Workflows: job design, task dependencies, triggers, and error management
• Designing incremental Medallion pipelines with quality rules and schema validation
• Integration with Git (GitHub/Azure DevOps), CI, and testing strategies for PySpark logic

Day 5 Lab — Build a Complete End-to-End Pipeline

• Assembling a Bronze→Silver→Gold pipeline orchestrated via Workflows
• Implementing logging, auditing, retries, and automated validations
• Executing the full pipeline, verifying outputs, and preparing deployment notes

Operationalisation, Governance, and Production Readiness

• Unity Catalog governance, lineage, and access control best practices
• Cost management, cluster sizing, autoscaling, and job concurrency patterns
• Deployment checklists, rollback strategies, and runbook creation

Final Review, Knowledge Transfer, and Next Steps

• Participant presentations of migration work and key takeaways
• Gap analysis, recommended follow-up actions, and handover of training materials
• References, further learning pathways, and support options